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AI Engineering Manager
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Build the agentic workforce for healthcare
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Magical is building an AI\-native automation platform for enterprise healthcare.
We believe healthcare organizations will not solve their operational problems by buying another 50 point solutions. They will build an agentic workforce: AI agents that can execute real work across fragmented systems, adapt as workflows change, and operate with the reliability, security, and observability healthcare requires.
That is what we are building.
Our work matters. Magical automations have helped identify more than 200 positive cancer cases, match suicidal veterans with mental health care, improve access to care, and help providers get paid in a $5\.2 trillion healthcare system still held together by too much manual work.
We are looking for a deeply technical, AI\-native Engineering Manager to lead a team of up to 12 engineers as we scale from early customer pull toward category\-defining execution.
The role
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This is not a traditional engineering management role.
We are not looking for someone whose primary contribution is running ceremonies, tracking tickets, or managing from a distance. We need a technical, product\-minded leader who can raise the quality and velocity of a team while remaining close to the architecture, the product, and the customer problems we are solving.
You will lead a team responsible for building and operating critical parts of Magical’s AI platform. You will set a high bar for execution, develop engineers, drive technical decisions, and work closely with Product, Design, Deployment, and GTM.
The right person can move from a one\-on\-one to an architecture review, unblock a production issue, challenge a roadmap decision, and help close an exceptional candidate without losing context or momentum.
What you will own
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- The performance, development, and effectiveness of a team of up to 12 engineers
- Technical execution across a meaningful area of Magical’s AI platform
- Engineering quality, delivery speed, ownership, planning, architecture reviews, and incident response
- The systems required to make probabilistic AI reliable enough for enterprise healthcare workflows
- Clear technical direction within your team, in partnership with senior engineers and engineering leadership
- Translating ambiguous product and customer problems into executable technical plans
- Improving the platform so customer deployments become faster, more repeatable, and more scalable
- Hiring, onboarding, coaching, and retaining exceptional engineers
- Creating an environment where engineers have high autonomy, clear accountability, and room to grow
- Cross\-functional execution with Product, Design, Deployment, Security, and customer\-facing teams
What we are looking for
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- Experience leading a high\-performing engineering team through an ambiguous, fast\-growing stage
- Strong technical judgment in production software systems, architecture, reliability, and operational excellence
- Meaningful experience with LLMs, agents, evals, orchestration, model behavior, or other production AI systems
- The ability to earn the respect of strong engineers without needing to make every technical decision yourself
- Strong product judgment and an instinct for balancing speed, quality, scalability, and customer impact
- A track record of developing engineers, addressing performance issues directly, and raising the talent bar
- Experience turning broad objectives into clear ownership, technical plans, and measurable outcomes
- Comfort operating close to customers and understanding the real\-world consequences of engineering decisions
- Familiarity with enterprise security and healthcare trust requirements, including HIPAA, SOC 2, PHI, permissions, auditability, logging, and data access
- Low ego, high standards, direct communication, and founder\-level urgency
Why this matters
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Agentic automation will not win in healthcare because the demos are impressive.
It will win when the systems are reliable, secure, observable, governed, and economically transformative.
That is the engineering problem.
As an Engineering Manager at Magical, you will help turn emerging AI capabilities into production systems that healthcare organizations can trust with their most important operational workflows.
You will have the opportunity to shape the team, the technical foundation, and the operating culture during a period of rapid growth.
What this role is not
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This is not the right role for someone who wants to manage from a distance.
We are not looking for:
- A process\-first manager whose primary value is ceremonies, reporting, and administration
- A big\-company operator who wants to introduce heavy process before the team needs it
- A manager who has moved too far away from architecture, technical tradeoffs, and production systems
- A pure AI researcher who does not want to own reliability, customers, and business outcomes
- A leader who avoids difficult performance conversations or allows standards to drift
- A project manager who coordinates work but cannot challenge the technical plan
- A backend\-only leader who does not want to engage deeply with Product, Deployment, and customer problems
- A manager who measures success by output rather than shipped outcomes, system quality, and team growth
We need someone technical enough to earn trust, decisive enough to create clarity, and hands\-on enough to help the team move faster.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At MAGICAL, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills in Demand for This Role
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $244,000 based on 23 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
MAGICAL AI Hiring
MAGICAL has 1 open AI role right now. They're hiring across AI Engineering Manager. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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